文献の詳細

論文の言語	英語
著者	Gourav Siddhad, Masakazu Iwamura, Partha Pratim Roy
論文名	Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach
論文誌名	IEEE Transactions on Artificial Intelligence
出版社	IEEE
査読の有無	有
年月	2025年12月
要約	Emotions are crucial in human life, influencing perceptions, relationships, behaviour, and choices. Emotion recognition using Electroencephalography (EEG) in the Brain-Computer Interface (BCI) domain presents significant challenges, particularly the need for extensive datasets. This study aims to generate synthetic EEG samples similar to real samples but distinct by augmenting noise to a conditional denoising diffusion probabilistic model, thus addressing the prevalent issue of data scarcity in EEG research. The proposed method is tested on the DEAP and SADT datasets, showcasing up to 5.6\% improvement in classification accuracy when using synthetic data with DEAP and similar positive results with SADT. This is higher compared to the traditional Generative Adversarial Network (GAN) based and Denoising Diffusion Probabilistic Model (DDPM) based approaches. This study further evaluates the effectiveness of state-of-the-art classifiers on EEG data, employing both real and synthetic data with varying noise levels, and utilizes t-SNE and SHAP for detailed analysis and interpretability. The proposed diffusion-based approach for EEG data generation appears promising in refining the accuracy of emotion recognition systems and marks a notable contribution to EEG-based emotion recognition.
DOI	10.1109/TAI.2025.3641576

• BibTeX用エントリー

  @Article{Siddhad2025,
    author =	{Gourav Siddhad and Masakazu Iwamura and Partha Pratim Roy},
    title =	{Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach},
    journal =	{IEEE Transactions on Artificial Intelligence},
    year =	2025,
    month =	dec,
    DOI =		{10.1109/TAI.2025.3641576},
    publisher =	{IEEE}
  }
  
  
  

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